Glioblastoma (Grade IV Astrocytoma; GBM) is a devastating cancer of the brain with median survival of 15 months, and 5% long-term survival. A key feature of GBM is its invasiveness: glioma cells spread from the primary tumor into the surrounding brain tissue by crawling through the brain micro-environment. If glioma cell crawling could be suppressed, it would potentially extend lifespan and increase the potential effectiveness for local and global therapeutic treatments. However, we do not adequately understand the mechanical and chemical basis of glioma migration in the brain. The goal of this project is to develop a mathematical/ computational model that will allow us to simulate glioma invasion on a computer, and, in the longer-term, perform virtual in silico drug screening. The model will have moderate complexity, with ~10-20 parameters, each representing a potential target for therapeutic intervention, either alone or in combination. To develop the model, we will start with an existing motor-clutch model that includes both environmental mechanics and chemistry, and has been partially tested experimentally using neurons and glioma cells on compliant hydrogels. In aim 1, the motor-clutch model will be further developed and tested on compliant hydrogels in vitro by interfering with motors and clutches, while also extending the stiffness range of the environment and testing cells obtained directly from the operating room. In aim 2, the model will be tested in vivo by quantifying cell migration in live mouse brain slices as a function of cell adhesiveness, micromechanical brain stiffness, and brain micro-architecture. In aim 3, engineered 2D and 3D microsystems will be fabricated to mimic the micro-confinement of brain tissue, which will provide more realistic in vitro systems intermediate in geometrical complexity between classic Petri dishes and animal models. To accomplish these aims requires building a highly interdisciplinary team with expertise in engineering, biology, and medicine. Overall, the project will establish the quantitative framework necessary to develop a model-driven approach to GBM, so that therapies can be designed and engineered with more predictable outcomes.